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Paper dusting has recently been recognized as a significant issue in the hygiene tissue industry. This review considers the factors that may contribute to dusting and linting, as well as the challenges of evaluating dusting propensity. Examining both the paper and textile industries, several methods of dusting and linting assessment exist. This review describes the equipment, methodologies, targeted paper grades or materials, advantages, and disadvantages. Standards (if applicable) are also covered for each of the major methods. These existing methods were found to have various drawbacks, suggesting a need for an improved, standardized method and device. In conclusion, and to motivate future research, mechanisms of dust release are reviewed along with existing and possible solutions to combat the tissue-dusting problem. This paper outlines the importance of creating a repeatable, standard, and accurate testing procedure and methodology for dust particle characterization in the hygiene tissue industry. [ABSTRACT FROM AUTHOR]

Thin-walled parts are widely used in the aerospace field. They have the characteristics of large material removal during processing and are affected by factors such as processing technology and processing environment, which can easily cause the deformation of parts. Deformation will seriously affect the machining accuracy of parts, which is one of the key factors affecting the installation accuracy and quality of parts. Aiming at the problem that the machining deformation of thin-walled parts cannot be perceived in real-time during the milling process. This paper constructs a digital twin framework for the milling process of thin-walled parts. Unity3D and Visual Studio 2010 software are used to build a digital twin intelligent processing platform to realize the interaction and integration of physical objects and virtual models. The simulation is carried out by ABAQUS finite element analysis software and verified by experiments. At the same time, based on the simulation data and historical processing data, the PSO-LSTM thin-walled part deformation prediction model is constructed, and the prediction model is embedded into the intelligent processing platform. The real-time monitoring of deformation during the machining process of thin-walled parts is realized, and the problem that the deformation of thin-walled parts cannot be perceived in real-time during the machining process is solved. By processing Al-7075-T6 thin-walled parts for experimental analysis, the prediction accuracy reaches 96.1%, which verifies the accuracy and effectiveness of the platform. [ABSTRACT FROM AUTHOR]

Manual repair tasks in the industry of maintenance, repair, and overhaul require experience and object-specific information. Today, many of these repair tasks are still performed and documented with inefficient paper documents. Cognitive assistance systems have the potential to reduce costs, errors, and mental workload by providing all required information digitally. In this case study, we present an assistance system for object-specific repair tasks for turbine blades. The assistance system provides digital work instructions and uses augmented reality to display spatial information. In a user study with ten experienced metalworkers performing a familiar repair task, we compare time to task completion, subjective workload, and system usability of the new assistance system to their established paper-based workflow. All participants stated that they preferred the assistance system over the paper documents. The results of the study show that the manual repair task can be completed 21% faster and with a 26% lower perceived workload using the assistance system. [ABSTRACT FROM AUTHOR]

As a technology for the interaction and integration of products and simulation models, the digital twin can achieve accurate prediction and evaluation of product performance. However, the accurate model base is computationally complex, has a long iteration time, and is unable to perceive changes in the operating state in time. This leads to poor adaptability of the model and low efficiency of performance evaluation. The surrogate model can simplify the above model and improve computational efficiency. Based on this, this paper proposes a digital twin modelling and updating approach. The surrogate model is applied to the digital twin modelling process, which can accurately describe the physical mechanism and achieve interaction with the physical world. Then, this paper defines the consistency metric function, which achieves the rapid perception of the operation state and follows the physical world. Meanwhile, an improved LHS-Adam model update algorithm is used to adaptively update the model structure, improving the efficiency of the model parameters adjustment. Finally, experiments are conducted on the bogie suspension system to verify the feasibility and effectiveness of the update method in practical applications. The experimental results show that the established digital twin model has good updating performance and more efficient performance evaluation capability. [ABSTRACT FROM AUTHOR]

In the workshop scheduling problem, frequent disturbances lead to continuous and frequent rescheduling. This is detrimental to the optimal utilization of production resources, the maximization of production efficiency, and the minimization of operational costs. Therefore, finding an effective method to reduce frequent rescheduling is crucial for stable and efficient workshop operations. This paper introduces digital twin (DT) technology. Serving as a digital replica of a physical system, DT establishes an interactive connection between the physical entity and its digital counterpart. It has been applied in multiple fields. A disturbance evaluation method for scheduling mechanisms based on DT technology is proposed and studied in this paper. Firstly, this method evaluates the impact of disturbances by using a causal factor chart (CFC) and convolutional neural networks (CNN). Then, corresponding scheduling mechanisms are proposed based on the degree of disturbance impact. DT technology is used to provide data and model support throughout the entire process. Through the experimental verification, workshop disturbances were accurately evaluated by this method. Two unnecessary instances of rescheduling were avoided, resulting in a 66.3% reduction in the time to handle disturbances. The experimental results show that the proposed method can enhance the adaptability of scheduling mechanisms and contribute to a more agile response to disturbances. [ABSTRACT FROM AUTHOR]

Traditional geometry optimization of elevator fairings is only based on computational fluid dynamics simulations to find optimal structure parameters, and a large volume of data generated during the elevator operation is not utilized to optimize elevator fairings collaboratively. This paper proposes a digital twin-driven design framework to design the elevator fairing of the next generation. A digital twin model corresponding to the real elevator is first established via a computing platform, and a multi-objective optimization method like neighborhood cultivation genetic algorithms is employed to optimize the elevator fairing design. The effectiveness of the digital twin-driven design framework is demonstrated by the elevator fairing design, and the results show that compared with the unoptimized elevator fairing, the air drag and the lateral force are lowered by 18.1% and 11.2%, respectively, and the turbulence coefficient decreases from 0.478 to 0.451 after optimizing the elevator fairing. [ABSTRACT FROM AUTHOR]

This paper investigates the cutting mechanism of stone–plastic composite through orthogonal cutting experiments of finite element simulations. In this work, special attention was given to chip formation and morphology, cutting force, temperature, and surface roughness. Four main types of chip morphology where modeled: cracking chip, element chip, segmental chip, and ribbon chip. The workpiece surface was smoothest in the case of ribbon chips, followed by segmental, element, and cracking chips. Heat generation, depth of cut, and cutting forces were directly proportional to one another. Experimental results and simulation results were in good agreement, confirming that the simulation model developed herein can be used for further investigation of orthogonal cutting processes. [ABSTRACT FROM AUTHOR]

In the present analysis, a new experimental pilot plant system for exploring resin-impregnated paper (RIP) bushing technology was conducted. Experimental and numerical studies were carried out on the resin infusion and impregnation processes in the paper condenser core of a RIP bushing. An innovative approach for the molding process (named molding method B) of a commercial RIP bushing was implemented. For this method, the homogeneity of the paper winding process was controlled precisely. The filling process was conducted by gravity due to the difference of heights between two vacuumed chambers and from bottom to top in the mold. The flow front position and infusion time were measured experimentally and used to obtain the axial permeability of the paper condenser core analytically and numerically. The results indicate that the molding methods have a significant effect on the RIP bushing quality, homogeneous filling, and impregnating process time. Also, it was found that homogeneity of the paper winding process and, consequently, uniform axial velocity of resin flow in the radial direction is the most effective factor to eject the air. Finally, it was recognized that the determination of axial permeability in the case of transient flow is novel in comparison with conventional procedures such as Darcy relation of steady flows. By decreasing the paper thickness from 0.3 to 0.2 mm, the permeability decreased from 8.84 × 10−10 to 4.52 × 10−10 m2. The microstructure of the core illustrates that the axial permeability of the crepe paper condenser core is considerably affected by the variation of the paper thickness. [ABSTRACT FROM AUTHOR]

Origami techniques derived from antique paper folding arts have been applied to solve engineering problems such as weight/space-saving structures and their rapid deployment for foldability. Recently, origami techniques' potential in mechanical reinforcement for structural designs has also been discussed. For example, the mechanical performance of origami tubular structures can be significantly improved compared with the circular and hexagonal tubes under lateral loading. In this paper, an origami-inspired tubular structure was applied as the infill pattern for additive manufacturing, and energy absorption performance under dynamic and static loadings was investigated. Fused filament fabrication was selected as the testbed for the origami-inspired infill pattern investigation. The samples with the origami infill pattern presented higher dynamic and quasi-static energy absorption performance than samples with grid or honeycomb infill patterns. The proposed structural reinforcement method can be expanded to crash-worthy structures, thin-wall structures, functional structures, and the design of lightweight, high-strength parts. [ABSTRACT FROM AUTHOR]

The vibration of the rolling mill has been a persistent issue affecting its safe and stable operation. To address the vibration problem in the F2 stand of a continuous rolling mill, this paper establishes a digital twin model of the rolling mill. Based on the digital twin model, a rolling mill virtual commissioning framework has been proposed to enhance the accuracy of real-time data and improve the utilization of production data, laying the foundation for reducing vibration during the operation of the rolling mill. In this process, we first construct a digital twin framework for the rolling mill, consisting of four levels: physical rolling mill, virtual rolling mill, twin data, and services. Next, an online data acquisition and virtual commissioning framework is established to achieve a state mapping from the physical rolling mill to the virtual rolling mill. Finally, using the digital twin model, the effects of real-time process parameter optimization are analyzed. The impact of process parameter optimization on the dynamic response of the rolling mill system is examined. The optimized rolling process parameters effectively reduce the vertical vibration of the rolling mill system. [ABSTRACT FROM AUTHOR]

In this paper, a non-supervised technique for on-line paper characterisation is presented. The method uses self-organising maps (SOM) and texture analysis for clustering different kinds of paper according to their properties. A light-through technique is used to get pictures of paper. Then, effective texture features are extracted from greyscale images and the dimensionality of the feature data is reduced with SOM allowing visual analysis of measurements. The method makes it possible to implicitly extract important information about paper formation. The approach provides excellent results. A classification error below 1% was achieved for four quality classes when local binary pattern (LBP) texture features were used. The improvement to the previously used texture features in paper inspection is huge: the classification error was reduced by over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view of the inspected data. [ABSTRACT FROM AUTHOR]

The paper industry manufactures corrugated cardboard packaging, which is unassembled and stacked on pallets to be supplied to its customers. Human operators usually classify these pallets according to the physical features of the cardboard packaging. This process can be slow, causing congestion on the production line. To optimise the logistics of this process, we propose a visual recognition and tracking pipeline that monitors the palletised packaging while it is moving inside the factory on roller conveyors. Our pipeline has a two-stage architecture composed of Convolutional Neural Networks, one for oriented pallet detection and recognition, and another with which to track identified pallets. We carried out an extensive study using different methods for the pallet detection and tracking tasks and discovered that the oriented object detection approach was the most suitable. Our proposal recognises and tracks different configurations and visual appearance of palletised packaging, providing statistical data in real time with which to assist human operators in decision-making. We tested the precision-performance of the system at the Smurfit Kappa facilities. Our proposal attained an Average Precision (AP) of 0.93 at 14 Frames Per Second (FPS), losing only 1% of detections. Our system is, therefore, able to optimise and speed up the process of logistic distribution. [ABSTRACT FROM AUTHOR]

In high-speed milling, the machining system is affected by a chatter resulting from the dynamic interaction between the tool and the part, which can cause harmful effects on the tool and the machined surface of the part. Chatter occurs more frequently for the milling of thin-walled parts due to their low stiffness. In addition, the dynamic characteristics of thin-walled parts vary along the tool path. The dynamics of the part are therefore the dominant factor that should be considered in the modeling and the study of the milling process and must be performed in 3D, where the third dimension is the tool position. This paper studies the milling stability of Al 7075-T6 thin-walled parts during high-speed milling considering the variation of dynamic characteristics and develops three-dimensional stability lobe diagrams of the spindle speed, axial depth of cut, and tool position. The dynamic equations of motion are solved numerically using semi-discretization method. Modal parameters of the tool and the part were extracted experimentally by modal tests. Then, cutting tests were conducted to validate the established model by measuring the machined surface roughness which is used as a criterion for detecting instability. The experimentally obtained results correspond well with the predicted stability limits. Moreover, the influence of different cutting parameters on the machining stability along the tool path was investigated. It is found that the variable spindle speed improves significantly the cutting process, and the best selection of feed per tooth impacts positively on the machined surface quality. [ABSTRACT FROM AUTHOR]

This paper investigated the cutting behaviors and chip formation in machining of carbon fiber reinforced polymer (CFRP) composites. The single cutting edge and unidirectional (UD) laminates of six different fiber orientations from 0° to 150° with an interval of 30° were employed in the orthogonal cutting test. The effects of fiber cutting angle, cutting speed and cutting depth on cutting mechanism and micro-damage evolution were estimated. Cutting force and thrust force obtained by dynamometer were used to evaluate the interaction between the tool and the workpiece. Subsurface damage was assessed by damage depth and failure mode of microstructure such as fiber and matrix. The machined surface roughness was used to characterize the machined surface quality. The results indicated that the average cutting force at θ > 90° was 7.58 times higher than that at θ < 90° and large deformed fibers below the machined surface existed elasticity recovery in the former case. The cutting speed and cutting depth had the most significant effects on the cutting force at θ = 90° where the fiber bending deformation and fiber kinking keep cutting force at a high level. The micromorphology of machined surface and subsurface revealed four typical cutting mechanisms, namely, interface-debonding, fracture-sliding, shearing-fracture, and bending-fracture in the range of θ = 0 ~ 180°. The variation trend of surface roughness with cutting parameters at θ > 90° is obvious and in consistent with that of cutting force. The most efficient factor on the roughness is found to be the fiber cutting angle accounting for 98.60%, followed by the cutting depth (0.55%) and cutting speed (0.25%). The interaction between the factors is not significant according to the results of ANOVA. [ABSTRACT FROM AUTHOR]

Smart structures of 4D printing are perhaps interdisciplinary research area with significant development and applicability future. The cornerstone of 4D printing is smart materials. When a stimulus is put in front of this structure, it changes into a different structure. This article initially provides a succinct insight of the aforementioned 4DP: SMs-SSs: 4D printing (4DP) technology imminent enlargements of smart material's (SMs) prospective to physique smart structure (SSs). Investigation into 4D printing has pulled in phenomenal enthusiasm. The research work of 4DP: SMs-SSs has stimulated promising future. The paper discusses smart materials and their potential towards mainstream 4D printing for smart constructions (4DP: SMs-SSs). Cursory research write-up on smart materials associated 4D printing approaches to process them based on adaptability to stimuli, fabrication, control mechanisms, multi physics modeling, and existing as well as emerging functionalities. Indeed, innovative structural initiatives could perhaps inspire new paradigms for stimulate positive structures. Novelty structures are being thoroughly researched, and new ideas will be incorporated. Programmability, reactivity toward and adaptability to their circumstances, and automation are all functionality of 4D-printed items. The article's conclusion is that 4DP: SMs that can create intelligent/smart structures (SSs) will even trigger a massive era of construction material. This article shows an insight into how quickly technology is changing, how some researchers and scholars are figuring out what it can do, and how engineers could use the ideas. The adoption of smart materials will assist in resolving the problem to a greater extent. 4D printing relies on shape memory alloys/polymers (SMAs/SMPs) as so forth organic materials. Imminent enlargements of smart materials prospective to physique smart structures are still being experimented with by scientists and engineers. [ABSTRACT FROM AUTHOR]

The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human–robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product. [ABSTRACT FROM AUTHOR]

The contact angle (CA) of a water droplet on the polished aluminum surfaces is usually between 60° and 90°. This paper discusses a tuned bulk micro-manufacturing approach for transforming the surface topology of aluminum simultaneously at the micro- and nano-length scales, thereby significantly influencing its wetting characteristics. The approach is cheap, easy to implement, and scalable. The treated aluminum surfaces appeared like roughly polished surfaces at the macroscale and were found to be robust and non-toxic. They exhibited a paper towel effect by showing extreme surface wettability (CA of zero) to many industrial liquids, i.e., wetting on these surfaces is only a function of the surface roughness but not the fluid type. This extreme wetting ability to multiple liquids (or ultra-omniphilicity) was generated by sequentially creating a sub-surface micro- and nano-cavity architecture with nano-cavities within the embryos of micro-cavities interconnected by a network of micro-grooves. [ABSTRACT FROM AUTHOR]

Nanocutting is a method of direct machining in nanomanufacturing field, and it is an important ultraprecision machining method. Nanocutting technology adopts single-point diamond cutting and nanoprobe scratching methods to make the machining accuracy of parts reach the nanometric scale. It is the method with the highest machining accuracy at present, so it is widely used in the processing of precision components. At the same time, nanocutting technology can be used to process nanostructures and provide processing methods for basic research in condensed matter physics. This paper summarized the cutting mechanism and cutting force in the nanocutting process, including the mechanism of nanocutting and brittle–ductile transition. The influence of RTS, temperature, wear and subsurface phase transformation on cutting performance is also discussed. At the same time, the characteristics of cutting force and friction coefficient during the cutting process are reviewed. After that, the simulation and experiment research of the nanocutting process are analyzed. At the end of the paper, the challenges and prospects of nanocutting technology are proposed and summarized. [ABSTRACT FROM AUTHOR]

Tool wear is unavoidable during machining, which is one of the most common tool failure modes. It is significant to evaluate the tool state quickly and effectively for timely tool change strategy. The cutting vibration signals after tool wear show strong non-Gaussian characteristics. Higher order spectrum is a powerful tool for analyzing the non-Gaussian characteristics of signals, and can restrain noise and provide more information than classical power spectrum analysis. This paper presents a milling tool wear state monitoring method based on higher order spectrum entropy. Due to the large amount of calculation of bispectrum, bispectrum diagonal slice is investigated. And the diagonal slice spectral entropy is proposed as tool wear indicator to monitor tool state. To verify the proposed method, cutting vibration signal of CNC machining center were collected and analyzed. The experimental results showed that the proposed approach can effectively monitor and diagnose the tool state, and has good robustness. It is feasible and effective for online monitoring milling tool wear. [ABSTRACT FROM AUTHOR]

The kraft manufacturing process is the main pulping process in the paper industry. The kraft chemical recovery process is an efficient technology that enables the recycling of the pulping chemicals and the generation of electrical power. However, this process presents substantial issues related to energy consumption and environmental emissions. One of the main fundamental elements of the kraft process is the lime kiln. Lime kiln gas consumption, SO2, and NOx air emissions are key factors from the energy saving point of view (i.e., energy efficiency) and environmental pollution in this industrial process, respectively. Knowledge of the process variables involved in a lime kiln and how these are related to gas consumption and air emissions is essential to predict the kiln's behavior and minimize its environmental effects. The aim of this research study is to build a regression model for each one of the three prime variables (gas consumption, SO2, and NOx emissions) of a lime kiln employed in the paper manufacturing process using the multivariate adaptive regression splines (MARS) method in combination with the artificial bee colony (ABC) technique. These two statistical learning techniques were combined, thereby obtaining an easy-to-interpret mathematical model with a high goodness-of-fit. A coefficient of determination greater than 0.9 is obtained for all the modeled variables. Moreover, the particular contribution or importance of the input variables in each model is also calculated. The results thus obtained are a useful instrument to gain a better understanding of the dynamics of the lime kiln and the involvement of the process variables in gas consumption and gas emissions. [ABSTRACT FROM AUTHOR]

Freehand sketch is a natural and intuitive communication channel for idea expression. Lines are drawn one after another to create the outline before additional lines are often drawn over the existing ones to make a sketch clearer. Typically, the sketch is transformed to be a 3D CAD model by a designer for use in subsequent operations. Sketch-based modeling has been researched to support this transformation but mainly for an online sketch on a digital device. For an offline sketch on a paper, commonly found used in practice, sketch-based modeling remains a challenge because a scanned image conceals enriched information on the sketch in a batch of data points. This paper proposes an approach for reconstructing a 3D model from a paper-based overtraced freehand sketch. Two main modules in this approach are single-line drawing identification and 3D reconstruction. The first module where image processing technique is applied is for transforming a paper-based overtraced freehand sketch to be a single-line drawing in order to generate more information about the sketch (i.e., the number of lines and their starting points and endpoints). The second module where progressive reconstruction approach with cubic corner is applied is for reconstructing a 3D model from the obtained single-line drawing. Steps to be taken in both modules have been formalized. The approach has been successfully implemented on LabVIEW, and tested with several samples. [ABSTRACT FROM AUTHOR]

Freehand sketching on a paper is commonly found to be used as it is a natural and intuitive communication channel for portraying ideas. Additional strokes are often drawn over the existing ones to make the sketch clearer. For its usefulness in subsequent steps, a sketch is transformed to be a 3D CAD model that is typically performed manually by a designer due to a lack of technology support. Geometrical reconstruction from an offline scanning of a sketch has been a challenge because enriched information obtained during sketching is reduced to a batch of data points. This paper presents an algorithm for identifying a single-line drawing from a paper-based overtraced sketch as it is the first important step towards geometrical reconstruction. Key activities in this approach are thick-line sketch creation, contour boundary extraction, segments identification, and line drawing creation. The algorithm has been successfully implemented on LabVIEW and tested with several samples. [ABSTRACT FROM AUTHOR]

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs). [ABSTRACT FROM AUTHOR]

Milling force during machining process has strong time-varying and multi-frequency characteristic, which is easy to cause tool vibration, wear, and cutting chatter, and even lead the machining accuracy of parts to be out of tolerance. The paper proposed a milling force prediction method for a general helix cutter during peripheral milling. In this model, the geometric parameters of milling cutter are parametric defined, and then an analytical instantaneous uncut chip thickness (IUCT) model is presented with taking cutter runout (offset and inclination) and geometric entity shape of workpiece into account. A series of milling experiments using aluminum alloy were carried out with six kinds of milling cutters in a high-speed machining center. The analysis results show that the predicted value of milling force can better reflect the change trend of the actual measured one, and well prove the validity of the presented method. Furthermore, the influences of milling parameters, cutter runout, milling type, cutter tooth number, and unequal spacing angle on the time domain and frequency domain excitation characteristics of milling force are systematically analyzed, which can provide useful guidance for the tool design and machining parameter selection in the future. [ABSTRACT FROM AUTHOR]

The global trend in manufacturing has shifted from a manufacturing-centric process toward a user-centric process. This has resulted in a shorter lifespan and a high product replacement rate of any consumer product. Germany has introduced the concept of Industry Revolution 4.0 (IR 4.0) to convert manufacturing processes and mechanisms into cyber-physical systems (CPS). Digital factory, being the first step into CPS and IR4.0, is being targeted as the most important evolution of the manufacturing industry. This paper defines digital factories and their differences between other similar domains such as smart factories, CPS, and virtual factories. The requirements and goals of a digital factory are explained in detail to facilitate future digital factory tool developments. Furthermore, the current challenges faced in the implementation of the digital factory are proposed to be approached by adapting an interoperable virtual reality technology. This paper emphasizes the usage of virtual reality (VR) in simulating a digital factory that aids in the decision-making and efficient operation of a manufacturing facility. Furthermore, recommendations gathered from previous studies for developing VR-based digital factory tools are also explained in detail in this paper. [ABSTRACT FROM AUTHOR]

Traditional bending products are usually divided into two steps: extrusion and bending. Staggered extrusion (SE) as a new process was proposed to prepare Mg alloy bending products in this paper. By designing the structure at one end of the stem, the dual functions of extrusion process and bending process can be realized in a single extrusion pass. The results of the present study show that by adjusting the degree of deformation of the AZ31 Mg alloy, a bending product with a specific curvature could be obtained by the SE process, and the microstructure of the bending product was refined. When the extrusion ratio was increased from 11.11 to 44.44, the grain size was refined from 23.89 to 9.69 μm, and the percentage of recrystallized structures increased from 61.1 to 78.8%. [ABSTRACT FROM AUTHOR]

Based on the proposed model, a multi-objective optimization framework is presented to optimize the feed rate with the objective of increasing the machining efficiency and the loading stability of the spindle. The in-process machining data generated in the machining process is calculated and aligned for building the data-based model of spindle power, and an artificial neural network (ANN)-based modeling approach is proposed for the spindle power. To validate the feasibility and advantage of the proposed methods, a set of machining experiments are conducted, showing that our proposed ANN-based model has good accuracy in terms of predicting the spindle power, as well as that the feed rate optimization framework as solved based on the multi-objective evolutionary algorithm based on decomposition (MOEA/D) can effectively improve the machining efficiency and reduce the fluctuation of spindle power. [Extracted from the article]

This paper presents a milling force model for carbon fiber-reinforced polymer (CFRP) multidirectional laminates based on segmented specific cutting energy. The proposed model simplifies the milling process of CFRP unidirectional laminates into multiple orthogonal cuttings. The CFRP specific cutting energy was determined by experimental segmentation. An empirical formula for the CFRP segmented specific cutting energy was established, and the milling force of the CFRP multidirectional laminates was predicted using the linear superposition principle. Experimental results confirmed the high accuracy of the milling force model. The specific cutting energy gradually decreased with increasing equivalent cutting thickness. With an increase in the fiber cutting angle θ, the specific cutting energy increased first and then decreased, peaking around θ = π/2. [ABSTRACT FROM AUTHOR]

In the paper-based packaging field, a sheet of paper material is introduced in a die cutter to create several creases and cuts. These creases allow to fold the sheet, and consequently, to build the final package. Thus, the quality of the crease is critical to get a valuable final product. In this work, a new system for automatic crease inspection is presented. The inspection is performed by processing crease visual information captured by a laser sensor. Automation is achieved by moving the laser with a cartesian robot along the sheet. A crease profile processing algorithm is proposed as well, which is able to extract geometric features from the different patterns that are associated with different paper types. Moreover, a crease classification methodology is proposed, where creases are grouped taking into account some physical properties. The experiments performed show the viability and usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

The B. John Davies Prize for the best paper published in IJAMT 2020 has been awarded to Yu YANG, Shimin MAO, Bo BAI, and Yuhua KUANG. It is applied directly to the free-form machine tool, eliminating the need for the equivalent conversion of machine settings from the cradle-type machine tool to the free-form machine tool, and taking advantage of the flexibility and freedom offered by the free-form machine tool. [Extracted from the article]

The countersink depth accuracy is a remarkable quality index in modern aerospace industry, and it needs to be controlled quite accurately due to the extremely tight tolerances. However, the wide use of the thin-walled workpiece with low stiffness makes it difficult to achieve the required tolerance because of the complex deformation in the countersinking process. Focusing on the accurate control of countersink depth in the drilling of thin-walled workpiece, this paper provides a novel insight into the study on predicting of the feasible workpiece deformation and compensating for the countersink depth error through both theoretical and experimental analyses. An analytical model of workpiece deformation is first presented with the Fourier series approach in this study, and the finite element simulation is carried out to verify the model precision. Then a flexible cutting force model is developed to help to analyze the effect of deformation on the thrust force in the countersinking process. A novel approach of integrated iterative algorithm, that considers both the deformation and the cutting force, is established to calculate the final feasible deformation and make decision for the countersink depth error compensation, and the generalized convergence and adaptability of this iterative algorithm are investigated based on the calculus theory. Finally, the proposed integrated methodology is verified by multivariate simulations and experiments, and the results show that the countersink depth accuracy could be effectively guaranteed by countersinking with compensation based on this integrated methodology. The work in this paper enables us to understand the causes of poor countersink depth accuracy in the drilling of thin-walled workpiece and develop new countersinking techniques to satisfy the tight tolerance. [ABSTRACT FROM AUTHOR]

The countersink accuracy problem caused by the deformation of thin-wall stacked structures is common in aircraft manufacturing, particularly for stacks with low stiffness. In the countersinking process of carbon fiber reinforced plastic (CFRP) and aluminum (Al) stacks, the cutting edge is quickly and severely worn due to the hard machinability of CFRP, resulting in the rapid increase of thrust force and then deformation. Focusing on the countersink accuracy control in CFRP/Al stacks, this paper first developed a CFRP countersinking thrust force model that considers tool wear and verified by experiments to help to investigate the stack deformation; then the micro peck strategy with quantitative analysis and theoretical modeling, which aims to decrease deformation and improve countersink accuracy, has been proposed based on the thrust force model and studied for its effectiveness in multivariate simulations and experiments. The research results indicate that both the increase of flank wear land of the tool and the decrease of stiffness of the stacks are the primary factors that undermine the countersink accuracy because they can affect the stack deformation, but using the micro peck strategy will minimize the adverse impact of these factors. The work in this paper includes two advantages: the micro peck method can be applied to the aeronautical manufacture with complex actual machining conditions; the theoretical model enables us to select appropriate peck parameters for the CFRP/Al stack countersinking process. [ABSTRACT FROM AUTHOR]

With the development and extensive applications of big data and artificial intelligence (AI), the manufacturing industry is gradually becoming increasingly intelligent. In the industrial sector, digital twin can greatly promote the innovation of products in the processes of design, production, operation, and maintenance. The traditional cam mechanism in mechanical reciprocating motion is constrained by the profile curve and lacks flexibility, which will be replaced by a new, flexible and controllable electronic cam. This paper designs a digital cam servo motion system based on digital twin. Using the multi-dimensional simulation software and taking advantage of virtual-real interaction ability of digital twin technology, the trajectory planning, state monitoring, and precise control of the electronic cam motion can be realized. The results show that the electronic cam servo motion control driven by digital twins has good observability, robustness, and adaptability. The scheme is feasible and practical, and provides reference for the design, manufacture, and application of electronic cam using digital twin technology. [ABSTRACT FROM AUTHOR]

Tool condition monitoring (TCM) is extremely important to ensure production efficiency and workpiece quality. It is crucial to extract and select suitable features from raw signals to improve the robustness and feasibility of TCM systems. This paper introduces a cutting force singularity analysis approach for TCM in milling, which correlates tool wear states with force waveform variations. The Holder exponents (HEs) were selected as the index of singularities. HEs are calculated by wavelet transform modulus maxima (WTMM). The raw signal is de-noised based on WTMM estimation, which can effectively preserve singularities compared with traditional filters. Fisher's discriminant ratio (FDR) is employed to rank the discriminant capability of statistical features of HEs. It is found that means of HEs and quantities of singular points estimated from feed forces Fx show the strongest class-discriminant ability. Then, these features were chosen as training samples to propose a TCM approach based on the support vector machine (SVM). Experimental results indicate that this approach provides reliable and effective advice for tool change. [ABSTRACT FROM AUTHOR]

Personalized production is a manufacturing concept relevant to the fourth industrial revolution, which can satisfy various customer needs inexpensively. There are three main hurdles to the efficient implementation of this concept: access, cost, and performance. This paper proposes a digital twin-based cyber physical production system (CPPS) architectural framework that overcomes the performance hurdle. The proposed architectural framework comprises five services that are solutions to the performance hurdle of personalized production, and it operates on information based on the proposed product, process, plan, plant, and resource (P4R) information model. This information model is manufacturing abstraction for personalized production and is presented on a detailed level with object-orientation and the "type and instance" concept. Whereas previous studies on the digital twin concept considered one or several facilities and focused on the development of independent applications, this study focuses on the digital twin as a core technological element of the entire system and analyzes CPPS design and its operation from the system-of-systems perspective. The application of the digital twin-based CPPS to a micro smart factory (MSF) provides an advanced solution for the personalized production of various products. An average makespan improvement of ~ 26.87% was achieved using the implemented CPPS services in the MSF. [ABSTRACT FROM AUTHOR]

Abrasive waterjet generation methods such as the conventional injection jet method and the suspension jet method have a strong influence on properties and costs of the machined products, particularly ones made of difficult-to-machine materials, e.g., ceramics. Since recycling may reduce the needed amount of materials significantly, the same holds true for recycling processes and technologies within the process chains of abrasive waterjet methods. In this paper, a methodology for integrated technical-economic evaluation of processes is presented and applied to the process technology of abrasive recycling in the suspension fine jet process chain aiming to show the technology's technical as well as economic potential. Research and development regarding such processes and technologies may contribute to gain competitive advantages for companies as they can differentiate from competitors by offering or processing materials with distinctive properties and/or lower prices causing customer's benefit. Consequently, it is also of great importance for companies applying waterjet cutting methods to know whether and which recycling technologies are promising from both a technical and an economic point of view. Experiments show that a significant share of the input material (abrasive particles) can be reused after recycling. The monetary appraisal reveals that technical potential results in considerably lower costs compared to the suspension jet method without recycling. [ABSTRACT FROM AUTHOR]

Self-excited chatter vibration is one of the most unexpected phenomena during the milling operation, which is always combined with time-varying and non-stationary characteristics. This paper presents a milling chatter detection approach combined with the time-frequency analysis (TFA) method and instantaneous frequency and energy aggregation characteristics of the chatter vibration in the milling process. A zoom synchrosqueezing transform (ZST)-based chatter identification approach and several chatter identification indicators are constructed for milling chatter identification. The TFA method ZST is used to characterize the time-varying and non-stationary characteristics of the chatter vibration. The zoom strategy is used to improve the time-frequency resolution and energy concentration of the obtained time-frequency distribution. From an energy aggregation characteristic perspective, 13 instantaneous frequency domain statistic indicators and an instantaneous energy ratio indicator based on the time-frequency distribution obtained by ZST are developed for milling chatter identification. Four groups of cutting tests with both end milling and peripheral milling are conducted to validate the effectiveness of the developed chatter identification indicators, and results show that the developed chatter identification indicators can effectively identify chatter in the milling process and are insensitive to the cutting parameters. [ABSTRACT FROM AUTHOR]

Sustainability in electronics has a growing importance due to, e.g. increasing electronic waste, and global and European sustainability goals. Printing technologies and use of paper as a substrate enable manufacturing of sustainable electronic devices for emerging applications, such as the multi-layer anti-counterfeit label presented in this paper. This device consisted of electrochromic display (ECD) element, NFC (near field communication) tag and circuitry, all fully roll-to-roll (R2R) printed and assembled on plastic-free paper substrate, thus leading to a sustainable and recyclable device. Our setup uses harvested energy from HF field of a smartphone or reader, to switch an electrochromic display after rectification to prove authenticity of a product. Our novelty is in upscaling the manufacturing process to be fully printable and R2R processable in high-throughput conditions simulating industrial environment, i.e. in pilot scale. The printing workflow consisted of 11 R2R printed layers, all done in sufficient quality and registration. The printed antennas showed sheet resistance values of 32.9±1.9 mΩ/sq. The final yield was almost 1500 fully printed devices, and in R2R assembly over 1400 labels were integrated with 96.5% yield. All the assembled tags were readable with mobile phone NFC reader. The optical contrast (ΔE*) measured for the ECDs was over 15 for all the printed displays, a progressive switching time with a colour change visible in less than 5 s. The smart tag is ITO-free, plastic-free, fully printed in R2R and has a good stability over 50 cycles and reversible colour change from light to dark blue. [ABSTRACT FROM AUTHOR]

Fillet end milling is widely used in mould, aerospace, shipping manufacture and other fields. As one of the important parameter in milling machining, milling force is related to machining efficiency and quality, as well as tool wear, but it is difficult to predict. Aiming at fillet end milling under a pre-determined inclination angle, considering cutter run-out, this paper presents a cutting force coefficients identification model related to chip thickness and axial position angle, and then achieve milling force prediction. A parametric relationship among feed direction, cutter axis and to-be-machined surface are established, and then, the analytic expression of previous swept surface, to-be-machined surface and cutter envelope surface is derived. Boundary curves of cutter workpiece engagement (CWE) are established based on spatial surface intersection analytically. The criterion of the cutter edge element involved in cutting is proposed. Judging element along the cutter edge one by one, whereafter the algorithm of in-cut-cutting edge (ICCE) is proposed. Combining mechanical force model with chip thickness considering feed direction and cutter run-out, a milling force prediction model is established. Based on the method of undetermined coefficients, combining average instantaneous measured force, coefficients corresponding to axial position angle and chip thickness are solved. Further instantaneous peak measured force is used to obtain cutter run-out parameters. Simulation and experiments show that CWE agrees well with that of experiment and ICCE is consistent well with that of simulation. Milling force experiments of different machining parameters show the milling force coefficients and run-out parameters can be applied to fillet end milling under pre-determined inclination angle well. [ABSTRACT FROM AUTHOR]

Building a high-precision aircraft assembly measurement field is one of the first prerequisites for completing the measurement work. The measurement field construction relies on the tooling layouts enhanced reference system (ERS) points. Therefore, the stability of the ERS point is essential. The ambient temperature affects the laid ERS points, resulting in an increase in the actual registration error at a later stage and the need for reinstallation, which undoubtedly increases the workload and seriously affects the efficiency of aircraft assembly measurements. To be able to predict ERS points registration errors in the design phase to guide the actual installation. Therefore, this paper proposes a method for predicting ERS points registration errors of large-size measurement fields based on finite elements and artificial neural networks. The technique effectively predicts ERS point deformation and registration errors to guide design. First, the finite element model generates the neural network training and test data set after completing the simulation analysis using different temperature conditions. Then, different neural network models are used to complete the data training. Finally, the performance and generalization ability of the artificial neural network are examined based on the test dataset. The results show that the trained artificial neural network can accurately and effectively predict the deformation of ERS points at different temperatures. By comparing the time consumption of the simulation and this paper's algorithm, this paper's algorithm has high efficiency relative to the simulation calculation. Finally, the registration error prediction analysis is completed by constructing the ERS point thermal deformation error matrix from the predicted deformation amount. The analysis results show that the method in this paper can effectively and quickly predict the ERS points registration error. The actual ERS points installation can be guided by the predicted registration error, thus effectively avoiding the instability caused by temperature on the ERS points. [ABSTRACT FROM AUTHOR]

The digital twin (DT) technology is currently considered a key technology for the digital representation of real-world systems. The application of DT technology in smart manufacturing can provide accurate model support for the analysis of mechatronic equipment applications based on model simulations. However, for such a mechanics-electric-hydraulic-control coupled complex system of mechatronics equipment, how to quickly and effectively construct its consistent multi-domain DT mechanism model has become the biggest obstacle to the wide application of DT technology in this field. Therefore, based on the synthesis of existing model construction methods for mechatronics equipment, this paper proposes multi-domain, multi-level, parametric, and consistent mechatronics equipment DT mechanism model construction guidelines. Based on the proposed model construction guidelines, a DT mechanism model construction process for mechatronics equipment is given. Finally, a consistent multi-domain DT mechanism model of computer numerical control machine tools (CNCMT) is constructed by Simscape, to verify the feasibility of the proposed method. The solutions in this paper provide a reliable, rapid, and consistent mechanism model along with construction guidelines and theoretical systems for engineers or researchers that use DT technology to solve specific application problems. Also, the implementation of the virtual commissioning application case in this paper provides application guidance for service analysis based on the DT model. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions during belt grinding is of great significance for environmentally friendly production in the manufacturing industry. In this paper, in order to better grinding aero-engine titanium alloy blades with the abrasive belt, an improved NSGA-II multi-objective optimization algorithm was proposed, which reduced the carbon emissions during the grinding process while ensuring the same surface roughness and material removal rate. Firstly, through analysis and finite element simulation, the model of abrasive belt grinding force is established and the rationality of the model is verified by experiments; furthermore, the carbon emission model of abrasive belt grinding and the multi-objective optimization model based on the improved NSGA-II algorithm are established; finally, the results of the algorithm are verified and compared through numerical simulation and experiments. Compared with the NSGA-II algorithm and the multiple objective particle swarm optimization algorithm, the optimization results of the algorithm in this paper have better diversity and uniformity and can find better non-dominated optimal solutions; the process parameters selected by the algorithm in this paper can more effectively reduce the carbon emissions during grinding. The optimization method proposed in this paper has certain reference significance for engineering practice. [ABSTRACT FROM AUTHOR]

Due to the discrete die of multi-point stretch bending, there are contact zone and non-contact zone on the profile, which makes the springback phenomenon more complicated. In this paper, considering that the neutral layer will shift due to the application of pre-stretching and post-stretching, an analytical model of the relationship between springback error and stress is established. Under the different bending radius, the maximum springback error obtained by theoretical calculation is 6.9%, while the average springback error of the traditional springback model is 10.5%, which means that the springback prediction model proposed in this paper is more reliable. Multi-point stretch bending has been unanimously recognized by everyone because of its adjustable die surface. In this paper, the secant method is used to calculate the springback compensation factor, so that the springback error of the profile is within the allowable error range of the production under the limited numbers of springback compensation. [ABSTRACT FROM AUTHOR]

The hot-dip galvanizing process is widely used in the production of steel products as an important anti-corrosion method for steel products. Due to the complexity of the galvanizing process and the immaturity of some technologies, some factory productions of hot-dip galvanizing products are often with defects such as unsuitable spangle size in the head and tail, unsuitable spangle size in the edge, diagonal edge, and penetration streaks (zinc flow lines), which seriously affect the appearance of the zinc layer and subsequent using function, and are likely to cause undesirable consequences. In order to solve these problems, the object detection method in the field of deep learning is applied to the zinc spangles defects detection. Firstly, a dataset of zinc spangles defects on zinc-coated steel surfaces was established, and the dataset is preprocessed in this paper. Data enhancement is used to improve the generalization ability of the model, and Wiener filter is used to reduce the motion blur. This paper combines transformer structure and Bi-FPN structure with the YOLO-v5 model to propose the YOLOv5-Transformer-BiFPN (YOLOv5-TB) model. The YOLOv5-TB model is used for zinc-coated steel defect detection, which can accurately and quickly detect spangles defects on the surface of zinc-coated steel. The experimental results show that the overall performance of YOLOv5-TB network applied to the detection of zinc spangles defects on zinc-coated steel surfaces is better than most of the current mainstream Object detection algorithms. [ABSTRACT FROM AUTHOR]

Global competition forces the process industries to continuously optimize plant operation. One of the latest trends in efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model- or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) is a new paradigm that has been known as a promising integrated technology in which distributed resources of manufacturing are integrated and transformed into manufacturing services and handled centrally. It permits multiple users to request services simultaneously by submitting required tasks to a manufacturing platform. The service composition and optimal selection (SCOS) is the fundamental issue of CMfg that aims to select appropriate services from available manufacturing cloud resources to complete the manufacturing task and satisfy the users. Surveying and analyzing the available studies on this NP-hard problem is highly desirable. Therefore, as far as we know, this paper is the first research that tries to investigate and discuss the CMfg-SCOS methods in a systematic way. In this regard, the selected studies have been classified into two distinct groups comprising multi-objective and single-objective techniques. Furthermore, this research provides a comprehensive investigation of the current articles and compares them from several aspects based on various factors such as QoS parameters, other criteria, their adopted datasets, simulation tools, and algorithms. Moreover, hot papers, journals, and authors in this field have been revealed. In addition, the open challenging and the lacks within this issue have been discussed which can be applied to upcoming research. [ABSTRACT FROM AUTHOR]